{"id":"W4389247780","doi":"10.12688/f1000research.130126.3","title":"Identification of high-performing antibodies for Moesin for use in Western Blot, immunoprecipitation, and immunofluorescence","year":2023,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Structural Genomics Consortium; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; Genentech; Ontario Genomics; National Institutes of Health; Government of Canada; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Mitacs; Genome Canada; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Moesin; Western blot; Immunofluorescence; Immunoprecipitation; Antibody; Cytoskeleton; Biology; Actin cytoskeleton; Cell biology; Virology; Computational biology; Immunology; Ezrin; Cell; Gene; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005437555,0.00269213,0.00177274,0.003110692,0.001888129,0.002028262,0.001865787,0.001762675,0.01061753],"category_scores_gemma":[0.005895829,0.001557204,0.001446988,0.002147499,0.0008880808,0.001255911,0.001384377,0.003436897,0.01351112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293323,"about_ca_system_score_gemma":0.001318744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001283665,"about_ca_topic_score_gemma":0.003469258,"domain_scores_codex":[0.9965031,0.0006551447,0.0006669427,0.0006520975,0.0009791865,0.0005436015],"domain_scores_gemma":[0.9958785,0.0007651715,0.0003095049,0.001006555,0.00175848,0.0002818633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001240355,0.0001048156,0.0003863484,0.0003485151,0.00003240323,0.0001151567,0.0001053277,0.0001033371,0.989762,0.0007313929,0.001797067,0.006389639],"study_design_scores_gemma":[0.00009594538,0.0003017738,0.008122018,0.0002313838,0.0001642363,0.0009902227,0.0001365254,0.00189794,0.9054884,0.0007710197,0.08174321,0.00005724617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1850393,0.01171078,0.7490171,0.001951505,0.001886587,0.006176777,0.01316696,0.005478614,0.02557242],"genre_scores_gemma":[0.1050177,0.007509372,0.8138158,0.001090499,0.0003171731,0.008511619,0.03971758,0.003313304,0.0207069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01061753,"threshold_uncertainty_score":0.03551918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0561545398543828,"score_gpt":0.3560723625635816,"score_spread":0.2999178227091988,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}